Result showed that the per-segment classification improves overall classification accuracy by more than 25% in comparison to the per-pixel approach.PathakV.DikshitO.Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International...
Compared with other state-of-the-art methods, our approach achieves superior classification performance, which clearly validates the advantages of the proposed method. 展开 关键词: time series classification segment-based features matching DOI: 10.1093/comjnl/bxs029 被引量: 13 ...
Automatic genre classification using large high-level musical feature sets This paper presents a system that extracts 109 musical features from symbolic recordings (MIDI, in this case) and uses them to classify the recordings by genre. The features used here are based on instrumentation, texture, ...
The Rectified Nearest Feature Line Segment (RN-FLS) classifier is an improved version of the Nearest Feature Line (NFL) classification rule. RNFLS corrects two drawbacks of NFL, namely the interpolation and extrapolation inaccuracies, by applying two consecutive processes-segmentation and rectification ...
Campbell. Compar- ison of segment and pixel-based non-parametric land cover classification in the brazilian amazon using multi-temporal landsat tm/etm+ imagery. Photogrammetric Engineering and Remote Sensing, 73(7), 2007.Budreski, K.A., Wynne, R.H., Browder, J.O., Campbell, J.B., 2007...
Genetic Programming (GP) is one of the successful evolutionary computation techniques applied to solve classification problems, by searching for the best classification model applying the fitness evaluation. The fitness evaluation process greatly impacts the overall execution time of GP and is therefore th...
An echo canceler has an adaptive filter with coefficients grouped into segments, and a candidate value memory that stores candidate values for each segment. Signal levels in the echo canceler are monitored to determine when reinitialization is necessary.
The final loss function is shown in formula 3, which is the sum of the sequence loss functions including classification (class probability), detection (Bounding Box), and segmentation (Mask). Experimental results In order to verify the effectiveness of the method, we conducted experiments on the...
DCNNBT: A novel deep convolution neural network-based brain tumor classification model. Fractals 2023, 31, 2340102. [Google Scholar] [CrossRef] Yousef, R.; Khan, S.; Gupta, G.; Siddiqui, T.; Albahlal, B.; Alajlan, S.; Haq, M.A. U-Net-based models towards optimal MR brain ...
A support vector machine is a supervised machine learning method developed by Cortes and Vapnik in 1995 for use in binary classification [38]. It has since been developed for use in regression, in which context it is called SVR [39]. In the domain of machine learning, SVR is considered an...